FRICTION MODELING OF Al-Mg ALLOY SHEETS BASED ON MULTIPLE REGRESSION ANALYSIS AND NEURAL NETWORKS

Abstract

This article reports a proposed approach to a frictional resistance description in sheet metal forming processes that enables the determination of the friction coefficient value under a wide range of friction conditions, without performing timeconsuming experiments. The motivation for this proposal is the fact that there exists a considerable amount of factors that affect the friction coefficient value and as a result building analytical friction model for specified process conditions is practically impossible. In this proposed approach, a mathematical model of friction behaviour is created using multiple regression analysis and artificial neural networks. The regression analysis was performed using a subroutine in MATLAB programming code and STATISTICA Neural Networks was utilized to build an artificial neural networks model. The effect of different training strategies on the quality of neural networks was studied. As input variables for regression model and training of radial basis function networks, generalized regression neural networks and multilayer networks, the results of strip drawing friction test were utilized. Four kinds of Al-Mg alloy sheets were used as a test material.

Modelling of Friction Phenomena Existed in Drawbead in Sheet Metal Forming

“…Among all training pairs (162 input signals and the corresponding output signal), 10% [ 33 , 77 ] were randomly selected and included in the validation set. The moment the RMS error value of the validation set no longer decreases was adopted as the criterion for completing the network training process [ 33 , 77 ]: where z i is the expected signal of the output neuron for the i -th pattern, y i is the signal of the output neuron for the i -th pattern, N is the number of vectors in the training set.…”

“…The prospects for the use of artificial intelligence in tribology have been discussed in the paper of Rosenkranz et al [ 32 ]. Lemu et al [ 33 ] applied radial basis function (RBF) ANNs to create a mathematical model of friction behaviour based on the results of the strip drawing test. They found that the back propagation algorithm is the most efficient learning algorithm.…”

Approaches for Preventing Tool Wear in Sheet Metal Forming Processes

“…In the process of single point incremental forming, owing to the point contact of the tool with the sheet, there are more severe contact conditions than in conventional SMF [73]. Aluminium and titanium sheets show a much greater tendency towards galling compared to steel sheets [74,75].…”

Using neural network for drowsiness detection based on EEG signals and optimization in the selection of its features using genetic algorithm

“…By changing the number of radial functions, the obtained results and the execution time of the plan are different [10,11]. Figure 6: RBF neural network [12] In RBF neural network, by changing the number of radial base functions, the obtained clustering rate is changed and the increase of the number of these functions is effective on its computation time. According to the obtained CCR, the best result in the number of radial functions is 15.…”